As our customers desire to include more data into their risk assessment capabilities, it becomes more complex in how a decision support system can show the inherent risk across many different data objects. Depending on your mandate, an agency (and an intelligence analyst) focuses on the domain visibility for those objects under their purview. Navies and Coast Guards are vessel centric, Customs and border administrations are shipment focused, and Immigration authorities are centered on people in their quest to extract anomalous behavior and high risk targets within their regulatory data.

GreenLine’s solutions have the ability to ingest and link related data objects to give our analysts and customers a more holistic view of risk. While Customs authorities have access to shipment data, they are often intrigued to see we can link the appropriate conveyance data (for routing, position, and situational awareness) as well as the data associated to the crew and passengers on that conveyance.

GreenLine’s framework for risk assessment works across all these data objects. The same works for a Navy in a second example. While Navies desire to quickly assess the risk of vessels in a certain geographical region, GreenLine can also present the risk associated with related shipments and people on the vessel(s) when available. The same could be performed for Immigration authorities. It all depends on your focus and starting point. Our customers are often ending up in the same repositories of data for: 1) People, 2) Shipment, 3) Conveyance. When you can ultimately create situational awareness or domain visibility under your purview, how does one assess the risk associated within all this related data? Our customers desire to see the data associated with all people on an aircraft /vessel/train/etc. including crew, and the cargo on that aircraft/vessel/train etc.. GreenLine has one approach that has resonated in our customer base:

We analyze the risk associated with all the related data objects and append a risk scorecard to each. The data is linked (using our logic for fusing and appending data) and presented in the Analyst’s dashboard (see figure 1 above) so the user can quickly discern the data object of greatest interest (usually the one that has scored red for highest risk) and drill into this information to review the risk indicators and rationale applied. In addition, the analyst can see if the conveyance or any associated commercial shipments should be scrutinized as well depending on the relationship to the other high risk data objects. So, if the system scores a shipment red/high risk, the crew = yellow/medium risk, and the vessel green/low risk; the analyst should explore the crew data first to see why there is an indication of a potential risk or threat. Secondly, the analyst can explore the risk scorecards for shipments and vessel to see if there is a linkage associated between these data objects and the crew. Perhaps a crew member is linked to a known drug organization. At the same time, perhaps there is one container or shipment on the vessel scoring red for a narcotics threat (laden in a drug source country, consigned to a suspicious consignee or importer, shipped to a P.O. Box or Hotel Suite, and the commodity is inconsistent with the container type). This leads one to ask if there is a relationship between the crew member and the container. Maybe there is, or maybe there isn’t, but the analyst should use these indications to confirm or negate their suspicions. Here is an example of an officer that did confirm his suspicions. It’s also an example of exemplary investigative work:

At one time in the 1990’s, Columbian drug cartels were moving cocaine up the western seaboard (via routings including Buenaventura, Panama, Manzinillo, LA, Seattle, Vancouver, etc.) by putting 50-100kg duffle bags in the front end of the container with a fake/counterfeit seal. Operatives within the port of arrival would break the seal, open the doors and extract the narcotics to smuggle into the country. The replacement seal was placed back on the container and the by the time a customs officer or other law enforcement showed interest, the drugs were gone and the container appeared un-tampered.

Figure 2 – Example Container Seal

I knew a law enforcement individual in one seaport who targeted Columbia origin containers and was successful in making many narcotics seizures. Over time the seizures stopped and the Columbia origin containers started arriving with another carriers’ seal on some of the containers. Secondly, the officer noticed the Columbian seal was now being placed on containers that had not originated in Columbia. When he inspected these containers he started to find the narcotics in duffle bags once again. Strangely enough, upon re-examination, the Columbia originating containers did indeed have the seal from this new shipping line. Surely, the drug interdictions were causing a shift in modus operandi, but what was going on?

Figure 3 – Seal Placement on Container Doors

Upon further investigation, this officer learned that the new containers had been taken off the vessel and re-handled in ports along the journey. The cocaine in duffle bags+ replacement seal was being removed from the Columbian container and placed on/in a second shipping line container that had originated from another country/another shipping line. The Columbian seal was being used to re-seal the new container with the contraband inside. The officer also learned that the 1st mate on the container ship had been in charge of stowage and was the only person with the responsibility and authority to re-handle containers in the earlier ports of call along the way.

Figure 4: Container re-handling (discharge onto the pier and then re-load) in the same port between origin and destination drew additional scrutiny in the case mentioned in this blog.

This story is an example of a direct linkage between a crew member and a shipment on the same cargo vessel. By highlighting the anomalies in both the crew and shipment, a more holistic view of risk can be presented. The same could and likely does occur in commercial aircraft, rail, and highway conveyances. Lookouts and risk indicator rules can be created one time only vs many times across people-based, cargo-based, or conveyance-based systems.

If a value proposition is of interest to you or your organization, please contact:

Chris Thibedeau,

VP WW Customs, Regulatory and Law Enforcement Solutions,

GreenLine Systems Canada

Ph: 011-613-884-8162 or email: chris.thibedeau@greenlinesystems.com

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About Chris Thibedeau

VP Worldwide Customs, Regulatory and Law Enforcement --
With over 23 years of experience and insight into border security, risk assessment, customs modernization, and the design and development of automated targeting systems, Chris is responsible for market development and acts as lead Subject Matter Expert for our Customs, Regulatory and Law Enforcement customers.
Chris previously held leadership roles in Enforcement, Operations and Major Projects with the Canada Border Services Agency (CBSA) where he was awarded a GTEC (Government of Canada Technology Award) gold medal and the Canadian Public Service Award of Excellence for his work leading the design and development teams responsible for the TITAN automated risk assessment system. Chris is a co-author of the World Customs Organization’s (WCO) Customs Risk Management Study, the Inter-American Development Bank’s Knowledge and Capacity Product (KCP) on Risk Management of Cargo and Passengers, and the WCO’s “Global Container Security and Identification of High Risk Indicators” that served as a core input to the General High Risk Indicator (GHRI) document. The GHRI now forms a major component of the “WCO Framework of Standards to Secure and Facilitate Global Trade.”
Chris holds a BA in English from Acadia University.